A prototype-regularized federated learning framework exchanges class-level prototypes and applies contrastive regularization to achieve better cross-domain ASTE performance while cutting communication costs.
In Findings of the Association for Computational Linguistics: ACL 2024, pages 10318–10329
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Prototype-Regularized Federated Learning for Cross-Domain Aspect Sentiment Triplet Extraction
A prototype-regularized federated learning framework exchanges class-level prototypes and applies contrastive regularization to achieve better cross-domain ASTE performance while cutting communication costs.